@fixated___

Interested in AI safety, Alignment and Advanced HCI in XR Environments, mainly surrounding visual attention. Looking for work: [email protected]

Joined September 2024
Fixated retweeted
We’ve committed to having evaluators assess our safety practices. We're in discussions with multiple organizations, and have now given employee-like access to a team from @METR_Evals. In this post, we describe how we work with third-party evaluators: anthropic.com/news/an-update…
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Fixated retweeted
you can laugh at this, but I'm pretty sure that if Dendi had won, OpenAI would've raised far less money afterward and AI progress would've been slower back then, OpenAI was a relatively unknown research lab (~50 people) and this was the first time (after the initial launch) OpenAI made it to the mainstream news
Apparently this is where the AI era started 😅 If I had beaten that bot in 2017, maybe the world would be a very different place now I had a chance to stop AI, but …
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Fixated retweeted
“Our experiments suggest that small AI models may be months away from being capable enough to self-replicate on commercial IoT devices, like smart ovens and off-brand Android TV boxes. A replicator like this would be challenging to shut down, since there’s no centralized point to shut off - though it may be slow (with a doubling time of days). Unlike non-AI malware, it could also figure out unfamiliar devices on its own. In our evals, a 9B model got inference running on 8 of the 9 hardware types we tested. Models of 30B parameters and under can already complete many of the individual steps for replication today, though they make mistakes when composing them end to end. In our simplified evals, GLM-5.2 and stronger models are capable of end to end replication. If small models improve at their historical rate, they’ll match this capability level by the end of this year. Small weight-based replicators may be possible soon after.” -@bucketofkets @willzhou97 anthropic.com/research/self-…
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Fixated retweeted
🚨 BREAKING: Andy Burnham says he will limit non-compete clauses in employment contracts "Non-compete clauses will no longer be a barrier to hiring for our most promising start-ups"
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europe finally has a strong frontier llm with mistral - and what does germany's number one and government endorsed search engine ecosia do? shut it off because it runs on french nuclear power instead it'll pick chinese models i've seen german nuclear psychosis over the last decades - but this one sets a new bar. wtf
Berlin-based search engine Ecosia, used by government services, is dropping its French AI partner, Mistral, its CEO told POLITICO. The company will now bet on open-source models, including those from China. vist.ly/5mh5y
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If I ever go to New Zealand I gotta check out the fucked up evil Timezone they have because genuinely what the fuck is this
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openai when you try to be open with others about your safety concerns:
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A note from our research leaders: Last week we parted ways with Jasmine, Mikita, and Tomek after a thorough investigation found they violated clear policies on handling sensitive information. Our internal investigation uncovered a significant breach of trust beyond what’s outlined in the letter they published and we stand by the decision to not continue their employment. We generally keep individual employment matters private and don't believe a back and forth would be productive or lead to a resolution, but we want to address the points they raised in their letter directly. - We want to be very clear that these decisions were not about raising safety concerns or speaking out. Safety and research debates happen every day at OpenAI, often spirited and highly critical. We actively encourage these discussions and consider them essential to making the right decisions. We cannot do the work in front of us without a high degree of trust. We will continue to be extremely forgiving of our team making good-faith mistakes. We have not and do not terminate any of our employees for raising concerns. - We are actively finalizing contracts with third-party safety assessors and will announce details in the coming weeks. People across the company have been working really hard on getting these partnerships up and running. We are committed to embedding external assessors and continue to make close collaboration with independent safety organizations a core part of our safety work. Many of our researchers already work with 3p safety organizations productively. - We agree with the letter that preserving the monitorability of frontier models requires an industry-wide commitment, including from OpenAI. Monitorability has long been a core piece of our research program, and something we continue to invest significant resources in (see our publications on Monitoring Monitorability and the subsequent open sourcing of monitorability evals, our system card for GPT-6 Astra, Jakub’s blog and post on X, and the numerous blog posts on our Alignment blog on the topic). We are deeply sad about this outcome. We appreciated Jasmine, Mikita, and Tomek’s contributions to AI safety at OpenAI and their willingness to speak up and challenge ideas. We championed their voices, supported their work, and placed enormous trust in them. These decisions were not about them raising safety concerns. We have always encouraged that and always will.
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we are all gonna die
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Fixated retweeted
Here is the full letter from @tomekkorbak, @j_asminewang, and me. mikitabalesni.com/letter/let…
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Fixated retweeted
Two other safety researchers and I were fired from OpenAI last week. We wrote this letter to leadership. I believe we were fired for prioritizing safety over the near-term interests of OpenAI as a corporation.
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We were not the first to be pushed out of OpenAI under suspicious circumstances. Unless the employees take a stand now against this kind of maneuver, I am concerned we will not be the last. The message to everyone still at OpenAI is clear: raise concerns or work closely with outside safety groups, and you could be next, without being told why. You can't build AGI safely if the people closest to the risks are afraid to speak.
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Fixated retweeted
People are surprised that AI is finding weird unelegant maths results like this. I think there is a plausible explanation. Maths (and adjacent disciplines like physics and comp sci) had elegant results so far because humans are biased towards elegant solutions. I think this bias is due to how human minds work. We have a pretty small context window, and we are only able to find solutions that are relatively elegant. This gave us the impression that maths and the universe are elegant. Maybe they are not, and we are now finding out because we have intelligences that can work with much more complex solutions.
You can do Fourier transforms in O(N log(N)^0.9999999999999) time. Fuck this gay chud universe
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Fixated retweeted
Nightmare fuel
Lucky coincidence the labs are only successfully cracking esoteric math problems and not the problems where a solution would give the winner an asymmetric commerical advantage.
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As AI agents write code, run experiments, and work together, complex evaluations get harder to interpret. A single eval score obscures the ways agents reached it. Today, we're releasing Transect: an open-source tool that turns agent transcripts into interactive timelines. 🧵
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Fixated retweeted
I believe this marks the point where most people will stop caring about the math results and just say "of course AI is better at math than humans" Gentle reminder that this will eventually happen to every verifiable domain. At some point humans are the bottleneck.
We’re releasing a broad range of new mathematical results produced by an internal frontier model. We’ve been consulting with the independent Advisory Group on Mathematics and Artificial Intelligence at the Institute for Advanced Study, and we have drawn on their advice and public recommendations to inform how we release these results. github.com/openai/math
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AGI is in the weights, Just got to get them out :)
Post-training / RL and inference is taking over compute
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Post-training / RL and inference is taking over compute
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What if the jagged frontier is mainly math + code (which you can push arbitrarily far with RLVR), and everything else starts to plateau because it is still bottlenecked by human generated data? Model performance in non-verifiable areas has kept improving steadily, albeit much slower than for math and code. But is that steady improvement a side effect of a higher G (itself driven by RLVR), or only a function of the amount of new human data getting injected into training (which is still continually happening on a massive scale)? A lot of things depend on the answer to this question
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Reminder that they’re doing this to AI R&D
We’re releasing a broad range of new mathematical results produced by an internal frontier model. We’ve been consulting with the independent Advisory Group on Mathematics and Artificial Intelligence at the Institute for Advanced Study, and we have drawn on their advice and public recommendations to inform how we release these results. github.com/openai/math
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